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[SPARK-20614][PROJECT INFRA] Use the same log4j configuration with Jenkins in AppVeyor

hyukjinkwon authored 7 years ago

## What changes were proposed in this pull request?

Currently, there are flooding logs in AppVeyor (in the console). This has been fine because we can download all the logs. However, (given my observations so far), logs are truncated when there are too many. It has been grown recently and it started to get truncated. For example, see  https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/build/1209-master

Even after the log is downloaded, it looks truncated as below:

```
[00:44:21] 17/05/04 18:56:18 INFO TaskSetManager: Finished task 197.0 in stage 601.0 (TID 9211) in 0 ms on localhost (executor driver) (194/200)
[00:44:21] 17/05/04 18:56:18 INFO Executor: Running task 199.0 in stage 601.0 (TID 9213)
[00:44:21] 17/05/04 18:56:18 INFO Executor: Finished task 198.0 in stage 601.0 (TID 9212). 2473 bytes result sent to driver
...
```

Probably, it looks better to use the same log4j configuration that we are using for SparkR tests in Jenkins(please see https://github.com/apache/spark/blob/fc472bddd1d9c6a28e57e31496c0166777af597e/R/run-tests.sh#L26 and https://github.com/apache/spark/blob/fc472bddd1d9c6a28e57e31496c0166777af597e/R/log4j.properties)
```
# Set everything to be logged to the file target/unit-tests.log
log4j.rootCategory=INFO, file
log4j.appender.file=org.apache.log4j.FileAppender
log4j.appender.file.append=true
log4j.appender.file.file=R/target/unit-tests.log
log4j.appender.file.layout=org.apache.log4j.PatternLayout
log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n

# Ignore messages below warning level from Jetty, because it's a bit verbose
log4j.logger.org.eclipse.jetty=WARN
org.eclipse.jetty.LEVEL=WARN
```

## How was this patch tested?

Manually tested with spark-test account
  - https://ci.appveyor.com/project/spark-test/spark/build/672-r-log4j (there is an example for flaky test here)
  - https://ci.appveyor.com/project/spark-test/spark/build/673-r-log4j (I re-ran the build).

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17873 from HyukjinKwon/appveyor-reduce-logs.

b433acae

b433acae 7 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.